Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer

Autor: Sundaram Suresh, Saras Saraswathi, Andrzej Kloczkowski, Vasiliy Sachnev, Rashid Niaz
Přispěvatelé: School of Computer Engineering
Jazyk: angličtina
Rok vydání: 2015
Předmět:
Candidate gene
Extreme learning machine
02 engineering and technology
Biology
Global cancer map
Bioinformatics
Biochemistry
Hallmarks of cancer
Pattern Recognition
Automated

03 medical and health sciences
Artificial Intelligence
Structural Biology
Neoplasms
Science::Medicine::Biomedical engineering [DRNTU]
Biomarkers
Tumor

0202 electrical engineering
electronic engineering
information engineering

Humans
Molecular Biology
Oligonucleotide Array Sequence Analysis
030304 developmental biology
Binary coded genetic algorithm
0303 health sciences
Artificial neural network
Applied Mathematics
3. Good health
Computer Science Applications
Gene Expression Regulation
Neoplastic

The Hallmarks of Cancer
YWHAZ
Cancer biomarkers
020201 artificial intelligence & image processing
Neural Networks
Computer

DNA microarray
Classifier (UML)
Algorithms
Research Article
Zdroj: BMC Bioinformatics
Popis: Background Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm. Results BCGA and ELM are combined and used to select a subset of genes that are present in the Global Cancer Mapping (GCM) data set. This set of candidate genes contains over 52 biomarkers that are related to multiple cancers, according to the literature. They include APOA1, VEGFC, YWHAZ, B2M, EIF2S1, CCR9 and many other genes that have been associated with the hallmarks of cancer. BCGA-ELM is tested on several cancer data sets and the results are compared to other classification methods. BCGA-ELM compares or exceeds other algorithms in terms of accuracy. We were also able to show that over 50% of genes selected by BCGA-ELM on GCM data are cancer related biomarkers. Conclusions We were able to simultaneously differentiate between 14 different types of cancers, using only 92 genes, to achieve a multi-class classification accuracy of 95.4% which is between 21.6% and 38% higher than other results in the literature for multi-class cancer classification. Our findings suggest that computational algorithms such as BCGA-ELM can facilitate biomarker-driven integrated cancer research that can lead to a detailed understanding of the complexities of cancer. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0565-5) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE